Background Agricultural supply chains are characterized by high transaction costs and agency risks stemming from information asymmetry and biological variability. Although blockchain is widely proposed as a solution, existing literature predominantly focuses on passive traceability rather than active algorithmic governance. Methods This study conducts a bibliometric synthesis of 367 documents (2018–2025) to map the field’s intellectual structure and research orientation. Co-occurrence analysis was employed to reveal distinct thematic clusters and identify the evolution of technological infrastructure in the sector. Results The analysis reveals a critical volume-impact paradox within the technological infrastructure group and a 16:1 asymmetry between traceability and automation research. This indicates a significant gap in leveraging smart contracts for economic enforcement and active supply chain management. Conclusion We propose the Agri-Cognito framework, a prescriptive architecture designed to bridge the cognitive void through AI-driven pre-consensus validation. The framework provides a theoretical blueprint for transitioning agricultural blockchains from passive digital logbooks to autonomous governance ecosystems, offering a direct response to the “oracle problem” and structural inefficiencies in current implementations.

As Web3 architecture, artificial intelligence (AI), immersive environments, and connected devices converge, societies are moving from digitally mediated interaction to digitally programmed organisations, reshaping how value, labour, identity, learning, and participation are produced and governed. Programmable money, decentralised identity, AI-driven avatars, digital twins, and immersive environments illustrate how programmability collapses traditional distinctions between infrastructure, governance, and social behaviour. While these systems offer significant potential for inclusion, efficiency, and innovation, they also introduce profound ethical risks. This means that ethical challenges should become core governance elements, as code, data, and automated systems increasingly mediate trust, agency, and power at scale. Ethical failures in digital systems can scale across platforms, populations, and jurisdictions. This paper conceptualises a structural shift in which institutional rules, incentives, and governance functions are increasingly executed directly within programmable digital infrastructure, making ethics, accountability, and trust intrinsic properties of system design.

Introduction The proliferation of dApps is increasing the attack surface for exploitable vulnerabilities in smart contracts, and thus there is a need for verifiable detection methodologies. Methods In this work, we propose a machine learning framework with blockchain integration for explainable and note that “explainable” implies “verifiable” smart contract vulnerability detection. The SmartBugs-curated data was systematically pre-processed with metadata filtering, feature correlation analysis and encoding for model evaluation. Four ensemble learning methods, Random Forest, XGBoost, LightGBM and CatBoost were tested under identical experimental settings for comparison. Results The Random Forest classifier initially achieved the best balance in terms of stability and performance with an accuracy of 87.67%, successfully detecting important vulnerability classes such as re-entrancy, unchecked low-level calls, etc. To enhance the applicability of our blockchain-based machine learning framework for vulnerable smart contract analysis we extend it from the initial 143-contract dataset SmartBugs-Curated to evaluate it on on large-scale set, namely, SmartBugs-Wild which contains 47,398 real-world Ethereum contracts. Based on 29 static contract-level features, unsupervised clustering (k = 4, silhouette score = 0.3735) identifies discrete structural archetypes present in the dataset. Ensemble classifiers (such as XGBoost, CatBoost, Random Forest and LightGBM) can get excellent discriminative performance on these cluster labels: LightGBM achieves 99% accuracy and 0.98918 macro-F1. Discussion The additional results show that the approach scales, is robust and leads to stable models, even if interpretable. After injecting SHAP-based explainability, the interpretability and predictive power of CatBoost became similar to those of Random Forest. In order to guarantee end-to-end trust and traceability of our optimised classifier, this was linked to a blockchain oracle that independently store the outcomes as well as confidence scores for predictions directly onto an Ethereum-compatible ledger through a Vulnerability Registry smart contract. This integration provides the data is immutable, auditable and transparent in reporting.

An agentic AI system could assist in reducing case workloads in the judiciary by providing a probable outcome for litigating parties and eliminating the need to go to court. However, agentic AI uses different ML and LLM models in its workflows, and these models may reflect biases present in their training data. These biases could affect the outcome of the pre-litigation proceedings, favouring one litigating party over the other, depending on demographic representations in the training dataset. We consider an Agentic AI marketplace where litigants can verify demographic representation in the training data of ML/LLM models used in each Agentic AI before selecting a suitable system for pre-trial analyses. ZKPs provide cryptographic primitives for verifying such claims without disclosing full information about the underlying dataset. This paper outlines a conceptual framework for performing these verifications within the European regulatory context. First, we demonstrate the implementation of an Agentic AI system for prelitigation analyses and then conceptualize an Agentic AI marketplace, outlining different technological interactions. We then formalise demographic representation as a verifiable property and outline a ZKP framework for verification, comprising different tech stacks like BBS+ signatures, bulletproofs, zk-SNARKs, and smart contract oracles. This paper presents a conceptual and architectural framework rather than an empirical system evaluation, and aims to establish a foundational design space for privacy-preserving bias verification in agentic legal AI.

The vaccine of blockchain technology has quickly altered the processing, validation, and settlement of health insurance claims by bringing transparency, automation, and tamper-proof data exchange. The traditional insurance claims systems continue to suffer from fragmented workflows, inconsistent data standards and high risk of fraud. This paper provides a detailed survey of the issues currently affecting claim management and the increasing importance of blockchain as a trust-enhancing technology. The present advancements in healthcare insurance blockchain research are established through a review of current literature that identifies required research areas. To enhance understanding of frauds, we organize healthcare insurance fraud into a framework that shows different attack methods that tokenization can protect. The fundamental principles of blockchain and data tokenization as healthcare assets are explained which led to the development of a complex survey system that operates on blockchain-based tokenization platforms. The research identifies five essential elements which include smart contracts, identity tokens, claim tokens, off-chain medical data orchestration, and interoperability systems as the core subjects of the study. The paper presents its research findings about digital transformation advantages which include fraud reduction and automated adjudication and real-time tracking and increased participant trust. The paper establishes an essential discussion about multiple obstacles which include universal token standardization issues and privacy concerns and legal compliance requirements that create scalability and interoperability challenges. The survey not only covers the technology, operations and regulation, but it also points out the future of health insurance as being secure, fast and easily scalable through tokenization.

Blockchain technology continues to promise transformative impact across domains such as supply chains, finance, and the Internet of Things (IoT). However, the rapid growth and increasing heterogeneity of blockchain platforms have made architectural decision-making progressively more complex for software architects. This study extends and updates a previous Multivocal Literature Review (MLR) to systematically identify and characterize active blockchain networks across foundational protocol layers. We analyze key architectural dimensions including consensus mechanisms, decentralization and access control models, smart contract support, block and ledger structures, interoperability features, and architectural lineage. Drawing on both academic and gray literature, we characterize a total of 147 blockchain networks spanning Layers-0 through-2. Our findings reveal an ecosystem largely driven by industrial innovation, with limited consolidation in the formal academic literature. The resulting architectural mappings aim to support software architects in making informed, evidence-based decisions when integrating blockchain technologies into software-intensive systems.

Decentralized Physical Infrastructure Networks (DePIN) utilize crypto-economic incentives to orchestrate the crowdsourced deployment and operation of real-world infrastructure. The design and long-term viability of their tokenomic systems are central to their potential but represent a complex and rapidly evolving field. This scoping review provides a structured synthesis of DePIN tokenomics, moving beyond descriptive mapping to organize its core design primitives into a coherent analytical model. Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, this review charts and synthesizes data thematically. Key findings reveal a prevailing “DePIN Flywheel” pattern grounded in a Burn-and-Mint Equilibrium, where demand is monetized through fiat-denominated usage credits created by burning the network’s native token. This mechanism, alongside governance-calibrated issuance and collateral requirements, forms the core of DePIN’s economic architecture. However, the literature consistently highlights significant challenges: the impact of token price volatility on provider economics, ensuring robust incentive alignment, generating sufficient non-speculative demand, and navigating regulatory uncertainty. We conclude with a research agenda prioritizing empirical event studies of governance changes, quality-adjusted reward measurement, and the development of valuation frameworks appropriate for these hybrid utility-governance assets.

Cryptocurrencies are alternative mediums of exchange to traditional fiat currencies, such as the dollar or the euro. A typical cryptocurrency is digital-only, is stored in distributed ledger technology (notably Blockchain), and conducts its transactions in a decentralized network system. While there exists a considerable amount of literature addressing the roles of cryptocurrencies and other decentralized assets in developed economies, there is a lack of effort in discovering the role of these assets in the developing world. To contribute to a holistic understanding of cryptocurrencies and their values/impacts, this review examines the use of cryptocurrencies in Africa, identifying key trends and sentiment over time. Following a systematic process of reviewing the literature, the authors identified 121 relevant papers on the subject. We found that in the early days, cryptocurrencies were primarily used to address specific financial challenges in Africa, such as high remittance costs and a large underbanked population. Later on, cryptocurrencies began to adopt additional roles, such as diversifying investment, and central bank digital currencies and other digital assets emerged as legitimate means of payment and financial services. Furthermore, using VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool, we identified four critical periods in the development of cryptocurrencies in Africa and the associated sentiment regarding this type of asset from the literature. Our findings contribute to a comprehensive understanding of the development and main use of cryptocurrencies in Africa.